size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9903406
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix" "array"
head( m )
x1 x2 x3
[1,] -1.7708808 -1.90946542 -1.95602031
[2,] -0.4884506 -0.41244693 -0.21389282
[3,] 0.1179213 0.06974808 -0.04337741
[4,] -0.4270466 -0.54134907 -0.28158861
[5,] -0.4173561 -0.06375022 -0.27566436
[6,] 0.4808077 0.48531890 0.54575294
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.996 0.004 0.993 -0.030 0.061 0.987
y3 0.996 1.000 0.004 0.987 -0.022 0.064 0.989
x2 0.004 0.004 1.000 -0.011 0.973 0.970 -0.008
y1 0.993 0.987 -0.011 1.000 -0.060 0.037 0.980
x1 -0.030 -0.022 0.973 -0.060 1.000 0.971 -0.041
x3 0.061 0.064 0.970 0.037 0.971 1.000 0.034
y2 0.987 0.989 -0.008 0.980 -0.041 0.034 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
Copyright © 2021 Biomedical Data Sciences (BDS) | LUMC